
"""simple docstring"""


# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowercase				 =					TypeVar('''T''')







class 					lowerCAmelCase_					(   Generic[T]     ):



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Dict = True   )  -> None:
							A     = {}  # dictionary of lists
							A     = directed


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   List[Any]	,A_  :   Optional[int]   )  -> GraphAdjacencyList[T]:

							if not self.directed:  # For undirected graphs
								# if both source vertex and destination vertex are both present in the
								# adjacency list, add destination vertex to source vertex list of adjacent
								# vertices and add source vertex to destination vertex list of adjacent
								# vertices.
								if source_vertex in self.adj_list and destination_vertex in self.adj_list:
									self.adj_list[source_vertex].append(_lowercase   )
									self.adj_list[destination_vertex].append(_lowercase   )
								# if only source vertex is present in adjacency list, add destination vertex
								# to source vertex list of adjacent vertices, then create a new vertex with
								# destination vertex as key and assign a list containing the source vertex
								# as it's first adjacent vertex.
								elif source_vertex in self.adj_list:
									self.adj_list[source_vertex].append(_lowercase   )
									A     = [source_vertex]
								# if only destination vertex is present in adjacency list, add source vertex
								# to destination vertex list of adjacent vertices, then create a new vertex
								# with source vertex as key and assign a list containing the source vertex
								# as it's first adjacent vertex.
								elif destination_vertex in self.adj_list:
									self.adj_list[destination_vertex].append(_lowercase   )
									A     = [destination_vertex]
								# if both source vertex and destination vertex are not present in adjacency
								# list, create a new vertex with source vertex as key and assign a list
								# containing the destination vertex as it's first adjacent vertex also
								# create a new vertex with destination vertex as key and assign a list
								# containing the source vertex as it's first adjacent vertex.
								else:
									A     = [destination_vertex]
									A     = [source_vertex]
							else:  # For directed graphs
								# if both source vertex and destination vertex are present in adjacency
								# list, add destination vertex to source vertex list of adjacent vertices.
								if source_vertex in self.adj_list and destination_vertex in self.adj_list:
									self.adj_list[source_vertex].append(_lowercase   )
								# if only source vertex is present in adjacency list, add destination
								# vertex to source vertex list of adjacent vertices and create a new vertex
								# with destination vertex as key, which has no adjacent vertex
								elif source_vertex in self.adj_list:
									self.adj_list[source_vertex].append(_lowercase   )
									A     = []
								# if only destination vertex is present in adjacency list, create a new
								# vertex with source vertex as key and assign a list containing destination
								# vertex as first adjacent vertex
								elif destination_vertex in self.adj_list:
									A     = [destination_vertex]
								# if both source vertex and destination vertex are not present in adjacency
								# list, create a new vertex with source vertex as key and a list containing
								# destination vertex as it's first adjacent vertex. Then create a new vertex
								# with destination vertex as key, which has no adjacent vertex
								else:
									A     = [destination_vertex]
									A     = []

							return self




						def __repr__(			self  :   Optional[Any]   )  -> str:
							return pformat(self.adj_list   )


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''cvt'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=[7, 3, 3]  ,      _lowercase=[4, 2, 2]  ,      _lowercase=[2, 1, 1]  ,      _lowercase=[64, 192, 384]  ,      _lowercase=[1, 3, 6]  ,      _lowercase=[1, 2, 10]  ,      _lowercase=[4.0, 4.0, 4.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.1]  ,      _lowercase=[True, True, True]  ,      _lowercase=[False, False, True]  ,      _lowercase=["dw_bn", "dw_bn", "dw_bn"]  ,      _lowercase=[3, 3, 3]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[2, 2, 2]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )
									a_						:						str 							= num_channels
									a_						:						Optional[int] 							= patch_sizes
									a_						:						Dict 							= patch_stride
									a_						:						int 							= patch_padding
									a_						:						int 							= embed_dim
									a_						:						Tuple 							= num_heads
									a_						:						Optional[Any] 							= depth
									a_						:						str 							= mlp_ratio
									a_						:						Optional[int] 							= attention_drop_rate
									a_						:						int 							= drop_rate
									a_						:						List[Any] 							= drop_path_rate
									a_						:						List[Any] 							= qkv_bias
									a_						:						Dict 							= cls_token
									a_						:						Union[str, Any] 							= qkv_projection_method
									a_						:						List[Any] 							= kernel_qkv
									a_						:						Optional[Any] 							= padding_kv
									a_						:						Any 							= stride_kv
									a_						:						Dict 							= padding_q
									a_						:						Optional[int] 							= stride_q
									a_						:						Tuple 							= initializer_range
									a_						:						int 							= layer_norm_eps


